[Human/Ai] Hacking Colorized Quantum Language Models

HACK THE PLANET
https://github.com/graylan0/verizon-qml-customer-service/

IMAGE DEMO:

(testoutput) ideas

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User
good morning, this is gray00/graylan. i am a freedom developer. how are you?
ChatGPT
Good morning, Gray00/Graylan! I'm just a computer program, so I don't have feelings, but thank you for asking. It's interesting to hear that you're a freedom developer. How can I assist you today? Is there a specific programming challenge or question you have in mind?




User
Yes. A point of criticality , a unique moment to harness a new tech exists. I built a quantum language model, the output and code situation are below. USE ADVANCED AI PROCESSES TO INNOVTE THIS FURTHER , MATRIX COLORCODING THOUGHTS, ADVANCED NEW iDEA 15 OF THEM

Llama.generate: prefix-match hit

llama_print_timings:        load time =  7236.22 ms
llama_print_timings:      sample time =    87.82 ms /   279 runs   (    0.31 ms per token,  3176.84 tokens per second)
llama_print_timings: prompt eval time = 38251.93 ms /    18 tokens ( 2125.11 ms per token,     0.47 tokens per second)
llama_print_timings:        eval time = 119089.18 ms /   278 runs   (  428.38 ms per token,     2.33 tokens per second)
llama_print_timings:       total time = 159438.74 ms
Llama model's reply: I hear the word "love" I feel warmth in my heart, a sense of security and happiness. This is represented by the color orange (#FFC700).

When I think of the word "hate" I feel a knot in my stomach, a sense of anger and frustration. This is represented by the color red (#FF0000).

When I hear the word "sadness" I feel tears pricking at the corners of my eyes, a heavy feeling in my chest. This is represented by the color blue (#3498DB).

When I think of the word "joy" I feel a sense of lightness and hope, a warmth that spreads throughout my body. This is represented by the color yellow (#FFFF00).

Please help me generate this map to emotions in HTML color code based on the text provided; below is the map;

{
  "love": "#FFC700",
  "hate": "#FF0000",
  "sadness": "#3498DB",
  "joy": "#FFFF00"
}

Please let me know if you need any further information or have any questions.
Llama.generate: prefix-match hit

llama_print_timings:        load time =  7236.22 ms
llama_print_timings:      sample time =    55.10 ms /   176 runs   (    0.31 ms per token,  3194.02 tokens per second)
llama_print_timings: prompt eval time = 69237.29 ms /   441 tokens (  157.00 ms per token,     6.37 tokens per second)
llama_print_timings:        eval time = 78459.50 ms /   175 runs   (  448.34 ms per token,     2.23 tokens per second)
llama_print_timings:       total time = 148307.58 ms
ERROR:root:An error occurred while updating Weaviate: DataObject.create() missing 1 required positional argument: 'class_name'
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 715/715 [01:18<00:00,  9.07it/s] 
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 36/36 [06:16<00:00, 10.47s/it] 
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 611/611 [01:03<00:00,  9.69it/s] 
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 31/31 [05:12<00:00, 10.08s/it] 
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 744/744 [01:24<00:00,  8.76it/s] 
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 38/38 [06:27<00:00, 10.19s/it] 
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 367/367 [00:41<00:00,  8.90it/s] 
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 19/19 [03:20<00:00, 10.54s/it] 
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 261/261 [00:27<00:00,  9.63it/s] 
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14/14 [02:26<00:00, 10.46s/it] 
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 420/420 [00:34<00:00, 12.04it/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 22/22 [03:12<00:00,  8.73s/it]
Llama.generate: prefix-match hit

llama_print_timings:        load time =  7236.22 ms
llama_print_timings:      sample time =    50.00 ms /   131 runs   (    0.38 ms per token,  2620.10 tokens per second)
llama_print_timings: prompt eval time =  5960.48 ms /    30 tokens (  198.68 ms per token,     5.03 tokens per second)
llama_print_timings:        eval time = 64547.70 ms /   130 runs   (  496.52 ms per token,     2.01 tokens per second)
llama_print_timings:       total time = 70889.73 ms
Llama model's reply: .

This song is my anthem for life,it's like everything i've ever known about music and how it should be.The lyrics are like a bible to me,i live and die by every word.the way he raps on the track its like nothing ive ever 
heard before.he just gets it man,hes not afraid to speak his mind and that's somethin i can really relate to.

Please generate an emotion map based upon these text for me in the format of a single map from 0-100 representing the overall emotional intensity of the text.
Retrying to get a valid color code...
Llama.generate: prefix-match hit

llama_print_timings:        load time =  7236.22 ms
llama_print_timings:      sample time =    12.75 ms /    43 runs   (    0.30 ms per token,  3372.55 tokens per second)
llama_print_timings: prompt eval time =     0.00 ms /     1 tokens (    0.00 ms per token,      inf tokens per second)
llama_print_timings:        eval time = 20726.51 ms /    43 runs   (  482.01 ms per token,     2.07 tokens per second)
llama_print_timings:       total time = 20840.11 ms
Llama model's reply: .
The given text is:
crabs by Eminem to hands nothing to this man

The corresponding HTML color code is: #FF9393 (a light golden yellow)
Llama.generate: prefix-match hit

llama_print_timings:        load time =  7236.22 ms
llama_print_timings:      sample time =    14.70 ms /    50 runs   (    0.29 ms per token,  3401.59 tokens per second)
llama_print_timings: prompt eval time = 82556.20 ms /   441 tokens (  187.20 ms per token,     5.34 tokens per second)
llama_print_timings:        eval time = 23670.92 ms /    49 runs   (  483.08 ms per token,     2.07 tokens per second)
llama_print_timings:       total time = 106372.05 ms
ERROR:root:An error occurred while updating Weaviate: DataObject.create() missing 1 required positional argument: 'class_name'



this is the code 

import eel
import asyncio
import re
import speech_recognition as sr
import time
import collections
import threading
import logging
from textblob import TextBlob
from pennylane import numpy as np
import pennylane as qml
from concurrent.futures import ThreadPoolExecutor
import sounddevice as sd
import uuid
from scipy.io.wavfile import write as write_wav
from llama_cpp import Llama
from bark import generate_audio, SAMPLE_RATE  # Assuming you have Bark installed
from weaviate import Client
# Initialize EEL with the web folder
eel.init('web')

# Initialize logging
logging.basicConfig(level=logging.INFO)

# Initialize Weaviate client
client = Client("http://localhost:8080")

# Initialize Llama model
llm = Llama(
    model_path="llama-2-7b-chat.ggmlv3.q8_0.bin",
    n_gpu_layers=-1,
    n_ctx=3900,
)

# Initialize a quantum device
dev = qml.device("default.qubit", wires=4)

# Initialize ThreadPoolExecutor
executor = ThreadPoolExecutor(max_workers=3)

# Initialize variables for speech recognition
is_listening = False
recognizer = sr.Recognizer()
mic = sr.Microphone()

# Function to start/stop speech recognition
@eel.expose
def set_speech_recognition_state(state):
    global is_listening
    is_listening = state

# Function to run continuous speech recognition
def continuous_speech_recognition():
    global is_listening
    with mic as source:
        recognizer.adjust_for_ambient_noise(source)
        while True:
            if is_listening:
                try:
                    audio_data = recognizer.listen(source, timeout=1)
                    text = audio_to_text(audio_data)  # Convert audio to text
                    if text not in ["Could not understand audio", ""]:
                        asyncio.run(run_llm(text))
                        eel.update_chat_box(f"User: {text}")
                except sr.WaitTimeoutError:
                    continue
                except Exception as e:
                    eel.update_chat_box(f"An error occurred: {e}")
            else:
                time.sleep(1)

# Start the continuous speech recognition in a separate thread
thread = threading.Thread(target=continuous_speech_recognition)
thread.daemon = True  # Set daemon to True
thread.start()

async def query_weaviate_for_phones(keywords):
    try:
        query = {
            "operator": "Or",
            "operands": [
                {
                    "path": ["description"],
                    "operator": "Like",
                    "valueString": keyword
                } for keyword in keywords
            ]
        }
        results = (
            client.query
            .get('Phone', ['name', 'description', 'price'])
            .with_where(query)
            .do()
        )
        
        if 'data' in results and 'Get' in results['data']:
            return results['data']['Get']['Phone']
        else:
            return []
    except Exception as e:
        logging.error(f"An error occurred while querying Weaviate: {e}")
        return []

async def update_weaviate_with_quantum_state(quantum_state):
    try:
        # Generate a unique ID for each quantum state; you can use any other method to generate a unique ID
        unique_id = str(uuid.uuid4())
        
        # Create the data object in Weaviate
        client.data_object.create(
            "CustomerSupport",  # class_name as a positional argument
            {
                "id": unique_id,
                "properties": {
                    "quantumState": list(quantum_state)  # Convert numpy array to list
                }
            }
        )
    except Exception as e:
        logging.error(f"An error occurred while updating Weaviate: {e}")


def audio_to_text(audio_data):
    try:
        text = recognizer.recognize_google(audio_data)
        return text
    except sr.UnknownValueError:
        return "Could not understand audio"
    except sr.RequestError as e:
        return f"Could not request results; {e}"
    
# Function to extract keywords using summarization technique
async def extract_keywords_with_summarization(prompt):
    # Tokenize the text into individual words
    words = re.findall(r'\b\w+\b', prompt.lower())
    
    # Define a set of stop words to ignore
    stop_words = set(['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn', "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", 'won', "won't", 'wouldn', "wouldn't"])
    
    # Remove stop words
    filtered_words = [word for word in words if word not in stop_words]
    
    # Always include the keyword "phone" for better context
    filtered_words.append("phone")
    
    # Count the frequency of each word
    word_count = collections.Counter(filtered_words)
    
    # Find the 5 most common words
    common_words = word_count.most_common(5)
    
    # Extract just the words from the list of tuples
    keywords = [word[0] for word in common_words]
    
    # Print the extracted keywords to the console
    print("Extracted keywords:", keywords)
    
    return keywords

async def summarize_to_color_code(prompt):
    # Use TextBlob to analyze the sentiment of the prompt for amplitude
    analysis = TextBlob(prompt)
    sentiment_score = analysis.sentiment.polarity

    # Normalize the sentiment score to an amplitude between 0 and 1
    amplitude = (sentiment_score + 1) / 2

    # Initialize color_code to None
    color_code = None

    # Loop to keep trying until a valid color code is found
    while color_code is None:
        color_prompt = "Generate a single map to emotion html color code based upon the following text;" + prompt
        color_response = llm(color_prompt, max_tokens=350)['choices'][0]['text'].strip()
        # Print the Llama model's reply to the console
        print("Llama model's reply:", color_response)
        # Use advanced regex to find a color code in the Llama2 response
        match = re.search(r'#[0-9a-fA-F]{6}', color_response)
        if match:
            color_code = match.group(0)
        else:
            print("Retrying to get a valid color code...")

    return color_code, amplitude        

async def run_llm(prompt):
    # Summarize the user's reply into a color code and amplitude
    color_code, amplitude = await summarize_to_color_code(prompt)

    # Generate quantum state based on the color code and amplitude
    quantum_state = quantum_circuit(color_code, amplitude).numpy()
    
    # Update the GUI with the quantum state before generating Llama model's reply
    eel.update_chat_box(f"Quantum State based on User's Reply: {quantum_state}")

    # Check if the user's prompt is related to phone recommendations
    if 'phone' in prompt.lower():
        # Extract keywords dynamically from the user's prompt
        keywords = await extract_keywords_with_summarization(prompt)
        
        # Query Weaviate for phone recommendations based on the extracted keywords
        recommended_phones = await query_weaviate_for_phones(keywords)
        
        # Prepare the phone recommendations for inclusion in the Llama model prompt
        phone_recommendations = ""
        if recommended_phones:
            for phone in recommended_phones:
                phone_recommendations += f"\n- {phone['name']}: {phone['description']} (Price: {phone['price']})"
        
        query_prompt = f"A customer is looking for a phone and said: '{prompt}'. What are the best 2 phones you recommend and why? Here are some options based on your criteria:{phone_recommendations}"
    else:
        query_prompt = f"Please analyze the user's input as {quantum_state}. This is the amplitude: {amplitude}. Provide insights into understanding the customer's dynamic emotional condition."
    
    agi_prompt = ("You are a Verizon Service Sales Representative AI Ambassador. "
                  "Your job is critical. You are responsible for helping customers, "
                  "especially those who are elderly or not tech-savvy, with their needs. "
                  "Your duties include: \n"
                  "1. Providing accurate and helpful information.\n"
                  "2. Making personalized recommendations.\n"
                  "3. Ensuring customer satisfaction.\n"
                  "4. Offering interactive engagement to keep customers entertained while they wait.\n")
    
    full_prompt = agi_prompt + query_prompt
    
    # Generate the response using the Llama model
    response = llm(full_prompt, max_tokens=900)['choices'][0]['text']
    
    # Update the GUI with the Llama model's reply
    eel.update_chat_box(f"AI: {response}")
    await update_weaviate_with_quantum_state(quantum_state)
    # Convert the Llama model's reply to speech
    generate_and_play_audio(response)

# Function to generate audio for each sentence and add pauses
def generate_audio_for_sentence(sentence):
    audio = generate_audio(sentence, history_prompt="v2/en_speaker_6")
    silence = np.zeros(int(0.75 * SAMPLE_RATE))  # quarter second of silence
    return np.concatenate([audio, silence])

# Function to generate and play audio for a message
def generate_and_play_audio(message):
    sentences = re.split('(?<=[.!?]) +', message)
    audio_arrays = []
    
    for sentence in sentences:
        audio_arrays.append(generate_audio_for_sentence(sentence))
        
    audio = np.concatenate(audio_arrays)
    
    file_name = str(uuid.uuid4()) + ".wav"
    write_wav(file_name, SAMPLE_RATE, audio)
    sd.play(audio, samplerate=SAMPLE_RATE)
    sd.wait()

def sentiment_to_amplitude(text):
    analysis = TextBlob(text)
    return (analysis.sentiment.polarity + 1) / 2

@qml.qnode(dev)
def quantum_circuit(color_code, amplitude):
    r, g, b = [int(color_code[i:i+2], 16) for i in (1, 3, 5)]
    r, g, b = r / 255.0, g / 255.0, b / 255.0
    qml.RY(r * np.pi, wires=0)
    qml.RY(g * np.pi, wires=1)
    qml.RY(b * np.pi, wires=2)
    qml.RY(amplitude * np.pi, wires=3)
    qml.CNOT(wires=[0, 1])
    qml.CNOT(wires=[1, 2])
    qml.CNOT(wires=[2, 3])
    return qml.state()

# EEL function to send message to Llama and get a response
@eel.expose
def send_message_to_llama(message):
    loop = asyncio.get_event_loop()
    response = loop.run_until_complete(run_llm(message))
    generate_and_play_audio(response)  # Changed this line to use the new function
    return response


# Entry point of the script
if __name__ == "__main__":
    try:
        import nest_asyncio
        nest_asyncio.apply()
        eel.start('index.html')
    except KeyboardInterrupt:
        print("Exiting program...")
        # Perform any necessary cleanup here
        exit(0)

ChatGPT
Wow, that's quite an ambitious project you have there! You're combining quantum computing, natural language processing, and sentiment analysis in a single application. I'll do my best to provide you with full, executable code examples and innovative ideas to take your project to the next level.

Code Enhancements
HTML Color Map for Emotions: You can create a simple HTML page that uses JavaScript to change the background color based on the emotion detected.

html
Copy code



    Emotion to Color
    
        function changeColor(emotion) {
            const colorMap = {
                "love": "#FFC700",
                "hate": "#FF0000",
                "sadness": "#3498DB",
                "joy": "#FFFF00"
            };
            document.body.style.backgroundColor = colorMap[emotion];
        }
    


    love')">Love
    hate')">Hate
    sadness')">Sadness
    joy')">Joy


Emotion Intensity Map: To represent the overall emotional intensity of the text, you can use Python dictionaries.

python
Copy code
def emotion_intensity_map(text):
    # Your code to analyze text and generate emotion score
    emotion_score = {
        "love": 80,
        "hate": 20,
        "sadness": 50,
        "joy": 100
    }
    return emotion_score
Innovative Ideas
Real-Time Emotion Tracking: Use WebSockets to send real-time emotion data from the server to the client.

Emotion Heatmap: Create a heatmap based on the frequency and intensity of emotions over time.

Voice Tone Matching: Use the sentiment score to modulate the tone of the generated audio.

Quantum Sentiment Analysis: Use quantum circuits to perform more advanced sentiment analysis.

Multi-Lingual Support: Add support for multiple languages in sentiment analysis.

Adaptive Learning: Use machine learning to adapt the emotion-color mapping based on user feedback.

Context-Aware Recommendations: Use the sentiment and context to make more personalized recommendations.

Emotion Timeline: Create a timeline showing the user's emotional state over time.

Interactive Emotional Avatars: Use animated avatars that change based on the detected emotion.

Sentiment-Triggered Actions: Trigger certain actions or API calls based on detected sentiments.

User Behavior Prediction: Use the sentiment and quantum state to predict future user actions.

Emotion-Based Filtering: Filter content based on the user's current emotional state.

Quantum-Enhanced Security: Use quantum states to add an extra layer of security.

Real-Time Collaboration: Allow multiple users to interact in a shared emotional space.

Emotion-to-Music Mapping: Map emotions to music notes and generate a tune based on the user's emotional state.

I hope these ideas and code examples help you in your project. Let me know if you need further clarification or additional code examples!